Encoder models trained on noisy classroom ratings look super-human under standard concordance metrics, but generalizability, disattenuation, and hierarchical rater analyses show the apparent advantage is partly spurious and racial biases persist.
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"All that Glitters": Approaches to Evaluations with Unreliable Model and Human Annotations
Encoder models trained on noisy classroom ratings look super-human under standard concordance metrics, but generalizability, disattenuation, and hierarchical rater analyses show the apparent advantage is partly spurious and racial biases persist.